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All Journal The Indonesian Journal of Computer Science
Haidar Ahmad Fajri
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Klasifikasi Serangan Jaringan menggunakan Teknik Imputasi Berbasis Jaringan Syaraf Tiruan Safrizal Ardana Ardiyansa; Eric Julianto; Natasha Clarissa Maharani; Haidar Ahmad Fajri
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4349

Abstract

Rapid technological developments have changed access to information significantly, especially in telecommunications. This growth creates new threats, such as network attacks, so detection becomes critical for network security. Leveraging machine learning algorithms to detect threats is promising, with effectiveness largely dependent on selecting relevant features optimized by the bat algorithm. Data imputation is critical in preparing data sets, and neural network-based imputation techniques demonstrate outstanding performance, achieving accuracy rates of 99.4% on validation data and 99.3% on test data. This method consistently maintains precision, recall, and scores around 98%. Models using this method also approach perfection in classifying normal and neptune labels. This imputation method can also be applied to other model architectures using autoML. Alternative models such as Light GBM, XGBoost, Random Forest, Extra Trees, and Weighted Ensemble L2 also exhibit exceptional accuracy, exceeding 99.8%.
Klasifikasi Sentimen Tweet dengan Arsitektur Hybrid Transformers-CNN pada Platform Twitter Safrizal Ardana Ardiyansa; Abdi Negara Guci; Jemmy Febryan; Dian Alhusari; Haidar Ahmad Fajri
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4653

Abstract

Twitter, now known as X, is a popular platform used to express opinions on the latest trends, making it a valuable source of data for sentiment analysis research. The huge volume of data makes manual analysis impractical because it requires a long time and human resources, so it is necessary to automate the sentiment classification process through machine learning. Machine learning can be used to classify sentiment on a large scale quickly and accurately by utilising patterns. Machine learning models such as Transformers-CNN show the most superior performance with accuracy reaching 85.71% on test data and 99.90% on training data. The accuracy on the test data was better than other architectures namely LSTM, CNN, BERT, Transformers-LSTM, and LSTM-CNN with accuracies of 84.73%; 82.27%; 77.34%; 85.71%; 84.24% respectively. Transformers-CNN also has a training time of 30.17 minutes which is shorter than Transformers-LSTM, but longer than the other architectures.
Segmentasi Citra Daun Tomat Berpenyakit dengan Metode K-Means Clustering pada Ruang Warna HSV Haidar Ahmad Fajri; Safrizal Ardana Ardiyansa; Eric Julianto
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4685

Abstract

Tomatoes have health benefits and high economic value, but are susceptible to diseases that can reduce yields by 50-60%. Early detection of tomato leaf diseases is necessary to reduce losses. Manual identification is time-consuming and costly, so an efficient technique is needed. This research proposes an image processing-based preprocessing technique using contrast stretching, clustering, background removal, and conversion to Hue-Saturation-Value color space. The results show that the proposed technique is able to identify septoria spot, mosaic virus, and bacterial spot, which are 94.99%, 92.83%, and 94.57%, respectively. Bacterial spot also had the highest sensitivity of 88.02%. This indicates that the technique is effective in detecting the disease, hovewer mosaic virus has a lower sensitivity of 82.53%. This value indicates that several cases were not correctly identified. Bacterial spot had the highest value of 87.74% in F_1-score followed by septoria spot at 87.01% and mosaic virus at 85.59%.
Co-Authors Abdi Negara Guci Dian Alhusari Eric Julianto Jemmy Febryan Natasha Clarissa Maharani Safrizal Ardana Ardiyansa